Will Wang’s spatial omics approach to studying neuromuscular diseases is unique.

He works at the intersection of biology and computer science to study how complex systems of cells interact, specifically focusing on the connections between nerves, muscles, and the immune response and their role in neuromuscular diseases.

We sat down with Wang, who recently joined the Institute as an assistant professor, to discuss his work and how computer technology is shaping the landscape of biomedical research.

How is your team taking advantage of computer technology to study neuromuscular diseases?

No cell exists in isolation. All our cells are organized into complex tissues with different types of cells interacting with each other. We study what happens at these points of interaction, such as where nerves connect to muscle cells. Combining many different types of data such as single cell sequencing, spatial proteomics, and measures of cell-cell signaling helps us get a more holistic look at how interactions between cells determine tissue function, as well as how these interactions are disrupted in injury and disease. Artificial neural networks help us make sense of these different types of data by finding patterns and insights the human brain can’t see on its own. And because computers can learn from the vast modality of data that we gather, we can also use them to help predict how biological systems will behave in the lab. The process goes both ways – from biology to computers and from computers to biology. 

How will these technologies shape the future of biomedical research?

Biology and computer programming are two different languages. There are a lot of mathematicians and programmers who are great at coming up with solutions to process data, but biological questions can get lost in translation and it’s easy to miss the bigger picture. And pure biologists don’t necessarily understand the full scope of what computers can do for them. If we’re going to get the most out of this technology in biomedical research, we need people with enough expertise in both areas that they can bridge the gap, which is what our lab is trying to do. Over time we’re going to see more and more labs that combine traditional biological experiments and data analysis approaches with artificial intelligence and machine learning. 

Are there any potential risks to these new technologies?

Artifical intelligence is here to accelerate discovery. Mundane tasks and measurements that took me weeks to carry out as a graduate student can be automated to a matter of minutes. We can now find patterns in high dimensional images that the human brain can’t easily visualize. However, any kind of artificial intelligence comes with a certain amount of risk if people don’t understand when and how to use the tools. If you just take the absolute word of the algorithm, there will inevitably be times where it’s not correct. As scientists, we use artificial intelligence as a cutting-edge discovery tool, but need to validate the findings in terms of the biology. At the end of the day, it is us, scientists, who are here to drive the discovery process and design real life experiments to make sure our therapies are safe and efficacious.